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docs(completion): standardize docstrings and document missing kwargs
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1 changed files with 95 additions and 34 deletions
129
litellm/main.py
129
litellm/main.py
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@ -5052,43 +5052,104 @@ def completion(
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) -> ModelResponse | CustomStreamWrapper:
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"""
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Perform a completion() using any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly)
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Parameters:
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model (str): The name of the language model to use for text completion. see all supported LLMs: https://docs.litellm.ai/docs/providers/
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messages (List): A list of message objects representing the conversation context (default is an empty list).
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OPTIONAL PARAMS
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functions (List, optional): A list of functions to apply to the conversation messages (default is an empty list).
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function_call (str, optional): The name of the function to call within the conversation (default is an empty string).
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temperature (float, optional): The temperature parameter for controlling the randomness of the output (default is 1.0).
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top_p (float, optional): The top-p parameter for nucleus sampling (default is 1.0).
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n (int, optional): The number of completions to generate (default is 1).
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stream (bool, optional): If True, return a streaming response (default is False).
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stream_options (dict, optional): A dictionary containing options for the streaming response. Only set this when you set stream: true.
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stop(string/list, optional): - Up to 4 sequences where the LLM API will stop generating further tokens.
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max_tokens (integer, optional): The maximum number of tokens in the generated completion (default is infinity).
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max_completion_tokens (integer, optional): An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
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modalities (List[ChatCompletionModality], optional): Output types that you would like the model to generate for this request.. You can use `["text", "audio"]`
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prediction (ChatCompletionPredictionContentParam, optional): Configuration for a Predicted Output, which can greatly improve response times when large parts of the model response are known ahead of time. This is most common when you are regenerating a file with only minor changes to most of the content.
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audio (ChatCompletionAudioParam, optional): Parameters for audio output. Required when audio output is requested with modalities: ["audio"]
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presence_penalty (float, optional): It is used to penalize new tokens based on their existence in the text so far.
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frequency_penalty: It is used to penalize new tokens based on their frequency in the text so far.
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logit_bias (dict, optional): Used to modify the probability of specific tokens appearing in the completion.
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user (str, optional): A unique identifier representing your end-user. This can help the LLM provider to monitor and detect abuse.
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logprobs (bool, optional): Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message
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top_logprobs (int, optional): An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.
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metadata (dict, optional): Pass in additional metadata to tag your completion calls - eg. prompt version, details, etc.
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api_base (str, optional): Base URL for the API (default is None).
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api_version (str, optional): API version (default is None).
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api_key (str, optional): API key (default is None).
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model_list (list, optional): List of api base, version, keys
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extra_headers (dict, optional): Additional headers to include in the request.
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Args:
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model (str): The name of the language model to use for text completion. See all supported LLMs:
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https://docs.litellm.ai/docs/providers/
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messages (list, optional): A list of message objects representing the conversation context. Defaults to [].
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timeout (float | str | httpx.Timeout | None, optional): The maximum execution time in seconds for the
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completion request. Defaults to None.
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temperature (float | None, optional): The temperature parameter for controlling the randomness of the output.
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Defaults to None.
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top_p (float | None, optional): The top-p parameter for nucleus sampling. Defaults to None.
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n (int | None, optional): The number of completions to generate. Defaults to None.
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stream (bool | None, optional): If True, return a streaming response. Defaults to None.
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stream_options (dict | None, optional): A dictionary containing options for the streaming response (e.g.
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{"include_usage": bool}). Only set this when stream is True. Defaults to None.
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stop (str | list[str] | None, optional): Up to 4 sequences where the LLM API will stop generating further
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tokens. Defaults to None.
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max_completion_tokens (int | None, optional): An upper bound for the number of tokens that can be generated
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for a completion, including visible output tokens and reasoning tokens. Defaults to None.
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max_tokens (int | None, optional): The maximum number of tokens in the generated completion. Defaults to None.
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modalities (list[ChatCompletionModality] | None, optional): Output types that you would like the model to
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generate for this request (e.g. ["text", "audio"]). Defaults to None.
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prediction (ChatCompletionPredictionContentParam | None, optional): Configuration for a Predicted Output,
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which can greatly improve response times when large parts of the model response are known ahead of time.
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Defaults to None.
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audio (ChatCompletionAudioParam | None, optional): Parameters for audio output. Required when audio output is
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requested with modalities: ["audio"]. Defaults to None.
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presence_penalty (float | None, optional): Penalizes new tokens based on their presence in the text so far.
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Defaults to None.
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frequency_penalty (float | None, optional): Penalizes new tokens based on their frequency in the text so far.
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Defaults to None.
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logit_bias (dict | None, optional): Modifies the probability of specific tokens appearing in the completion.
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Defaults to None.
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user (str | None, optional): A unique identifier representing your end-user to monitor and detect abuse.
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Defaults to None.
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reasoning_effort (str | None, optional): Constrains effort on reasoning for supported reasoning models (e.g.,
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o1, o3-mini). Accepted values: "none", "minimal", "low", "medium", "high", "xhigh", "max", "default".
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Defaults to None.
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verbosity (str | None, optional): Verbosity level for reasoning or output tokens ("low", "medium", "high").
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Defaults to None.
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response_format (dict | type[BaseModel] | None, optional): Configuration for structured outputs or JSON mode.
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Can be a dictionary (e.g. {"type": "json_object"} or {"type": "json_schema", ...}) or a Pydantic
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BaseModel class. Defaults to None.
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seed (int | None, optional): Seed for deterministic sampling. Repeated requests with the same seed and
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parameters aim to return the same result if supported. Defaults to None.
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tools (list | None, optional): A list of tools the model may call. Defined in the provider schema (e.g.,
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[{"type": "function", "function": {...}}]). Defaults to None.
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tool_choice (str | dict | None, optional): Controls which tool is called by the model ("none", "auto",
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"required", or a specific function schema). Defaults to None.
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logprobs (bool | None, optional): Whether to return log probabilities of the output tokens. Defaults to None.
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top_logprobs (int | None, optional): An integer between 0 and 5 specifying the number of most likely tokens
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to return at each token position, each with an associated log probability. Requires logprobs=True.
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Defaults to None.
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parallel_tool_calls (bool | None, optional): Whether to enable parallel function/tool calling during tool use.
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Defaults to None.
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web_search_options (OpenAIWebSearchOptions | None, optional): Configuration options for web search grounding.
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Defaults to None.
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include_server_side_tool_invocations (bool | None, optional): Whether to include server-side tool
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invocations in the response. Defaults to None.
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deployment_id (str | None, optional): Provider deployment identifier (e.g. Azure deployment). Defaults to None.
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extra_headers (dict | None, optional): Additional HTTP headers to include in the request. Defaults to None.
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safety_identifier (str | None, optional): Identifier used for provider safety evaluations. Defaults to None.
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service_tier (str | None, optional): Specifies the service latency tier (e.g., "auto", "default").
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Defaults to None.
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store (bool | None, optional): Whether to store the output of this request for model distillation or evals.
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Defaults to None.
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prompt_cache_key (str | None, optional): Key identifier for prompt caching. Defaults to None.
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functions (list | None, optional): Deprecated OpenAI function definitions. Use tools instead. Defaults to None.
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function_call (str | None, optional): Deprecated OpenAI function call control. Use tool_choice instead.
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Defaults to None.
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base_url (str | None, optional): Base URL for the API endpoint (alias for api_base). Defaults to None.
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api_base (str | None, optional): Base URL for the API. Defaults to None.
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api_version (str | None, optional): API version for requests. Defaults to None.
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api_key (str | None, optional): API key for authentication. Defaults to None.
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model_list (list | None, optional): List of API bases, versions, and keys for load balancing/fallbacks.
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Defaults to None.
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thinking (AnthropicThinkingParam | None, optional): Configuration for thinking budget for models supporting
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extended thinking (e.g., Anthropic Claude). Defaults to None.
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shared_session (Optional[ClientSession], optional): Optional aiohttp ClientSession to reuse across requests.
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Defaults to None.
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enable_json_schema_validation (bool | None, optional): Per-request toggle to validate structured
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output schemas. Defaults to None.
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drop_params (bool | None, optional): If True, silently drop parameters unsupported by the target provider
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instead of raising an error. Defaults to None.
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additional_drop_params (list[str] | None, optional): List of additional parameter names to drop if unsupported.
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Defaults to None.
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mock_response (str | None, optional): If provided, return a mock completion response for testing or debugging.
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Defaults to None.
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custom_llm_provider (str | None, optional): Explicit provider override for non-OpenAI LLMs (e.g., "bedrock",
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"vertex_ai"). Defaults to None.
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max_retries (int | None, optional): Number of retries to attempt on failure. Defaults to None.
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fallbacks (list | None, optional): List of fallback models to try if the primary model call fails.
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Defaults to None.
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metadata (dict | None, optional): Additional metadata dictionary to tag completion calls. Defaults to None.
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**kwargs: Additional provider-specific keyword arguments passed to the underlying LLM call.
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LITELLM Specific Params
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mock_response (str, optional): If provided, return a mock completion response for testing or debugging purposes (default is None).
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custom_llm_provider (str, optional): Used for Non-OpenAI LLMs, Example usage for bedrock, set model="amazon.titan-tg1-large" and custom_llm_provider="bedrock"
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max_retries (int, optional): The number of retries to attempt (default is 0).
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Returns:
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ModelResponse: A response object containing the generated completion and associated metadata.
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ModelResponse | CustomStreamWrapper: A response object containing the generated completion and associated
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metadata, or a streaming generator if stream=True.
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Note:
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- This function is used to perform completions() using the specified language model.
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